Update 2023

.github/workflows/ci.yml

@@ -21,31 +21,30 @@ jobs:
       fail-fast: false
       matrix:
         cfg:
-          - os: ubuntu-latest
-            python-version: "3.7"
           - os: ubuntu-latest
             python-version: "3.8"
-          #- os: ubuntu-latest
-          #  python-version: "3.9"
+          - os: ubuntu-latest
+            python-version: "3.9"
           #- os: macos-latest
-          #  python-version: "3.6"
+          #  python-version: "3.9"
           #- os: windows-latest
-          #  python-version: "3.6"
+          #  python-version: "3.9"
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
 
       # More info on options: https://github.com/conda-incubator/setup-miniconda
-      - uses: conda-incubator/setup-miniconda@v2
+      - uses: conda-incubator/setup-miniconda@v3
         with:
           python-version: ${{ matrix.cfg.python-version }}
-          mamba-version: "*"
+          miniforge-variant: Mambaforge
+          miniforge-version: latest
           environment-file: environment.yml
           channels: conda-forge,defaults
           activate-environment: kinfraglib
-          auto-update-conda: false
-          auto-activate-base: false
-          show-channel-urls: true
+          #auto-update-conda: false
+          #auto-activate-base: false
+          #show-channel-urls: true
 
       - name: Additional info about the build
         shell: bash
@@ -64,7 +63,7 @@ jobs:
         shell: bash -l {0}
         run: |
           echo "Download combinatorial library from zenodo..."
-          wget -q -O data/combinatorial_library/combinatorial_library.tar.bz2 https://zenodo.org/record/3956580/files/combinatorial_library.tar.bz2?download=1
+          wget -q -O data/combinatorial_library/combinatorial_library.tar.bz2 https://zenodo.org/record/10843763/files/combinatorial_library.tar.bz2?download=1
           ls -l data/combinatorial_library/
           echo "Decompress selected files..."
           tar -xvf data/combinatorial_library/combinatorial_library.tar.bz2 combinatorial_library_deduplicated.json chembl_standardized_inchi.csv
@@ -75,6 +74,6 @@ jobs:
       - name: Run tests
         shell: bash -l {0}
         run: |
-          PYTEST_ARGS="--nbval-lax --current-env --nbval-cell-timeout=900"
+          PYTEST_ARGS="--nbval-lax --nbval-current-env --nbval-cell-timeout=1800"
           pytest $PYTEST_ARGS
 

CITATION.bib

@@ -0,0 +1,12 @@
+@article{doi:10.1021/acs.jcim.0c00839,
+author = {Sydow, Dominique and Schmiel, Paula and Mortier, Jérémie and Volkamer, Andrea},
+title = {KinFragLib: Exploring the Kinase Inhibitor Space Using Subpocket-Focused Fragmentation and Recombination},
+journal = {Journal of Chemical Information and Modeling},
+volume = {60},
+number = {12},
+pages = {6081-6094},
+year = {2020},
+doi = {10.1021/acs.jcim.0c00839},
+note ={PMID: 33155465},
+URL = {https://doi.org/10.1021/acs.jcim.0c00839}
+}

README.md

@@ -4,7 +4,9 @@
 
 ![KinFragLib workflow](./docs/img/toc_github_kinfraglib.png)
 
-Please note that this repository is constantly updated. You can retrieve the repository state for the published KinFragLib paper in release [v1.0.0](https://github.com/volkamerlab/KinFragLib/releases/tag/v1.0.0).
+**Note**: This repository is constantly updated, hence the statistics and numbers derive from the paper. 
+The current fragmentation library is based on the [KLIFS](https://klifs.net/) database downloaded on 06.12.2023. 
+You can retrieve the repository state for the published KinFragLib paper in release [v1.0.0](https://github.com/volkamerlab/KinFragLib/releases/tag/v1.0.0).
 
 ## Table of contents
 
@@ -61,8 +63,13 @@ fragments in order to generate novel potential inhibitors.
     # Change to KinFragLib directory
     cd /path/to/KinFragLib   
  
-    # Create and activate environment
+    # Create environment
+    # Hint: if conda is too slow, consider mamba instead
     conda env create -f environment.yml
+    # When using a MacBook with an M1 chip you may need instead:
+    CONDA_SUBDIR=osx-64 conda env create -f environment.yml
+
+    # Activate environment
     conda activate kinfraglib
 
     # Install the kinfraglib pip package
@@ -85,7 +92,7 @@ fragments in order to generate novel potential inhibitors.
 Please contact us if you have questions or suggestions.
 
 * Open an issue on our GitHub repository: https://github.com/volkamerlab/KinFragLib/issues
-* Or send us an email: andrea.volkamer@charite.de
+* Or send us an email: volkamer@cs.uni-saarland.de 
 
 We are looking forward to hearing from you!
 
@@ -95,7 +102,7 @@ This resource is licensed under the [MIT](https://opensource.org/licenses/MIT) l
 
 ## Citation
 
-Sydow, D., Schmiel, P., Mortier, J., and Volkamer, A. KinFragLib: Exploring the Kinase Inhibitor Space Using Subpocket-Focused Fragmentation and Recombination. J. Chem. Inf. Model. 2020. https://pubs.acs.org/doi/abs/10.1021/acs.jcim.0c00839
+[Sydow, D., Schmiel, P., Mortier, J., and Volkamer, A. KinFragLib: Exploring the Kinase Inhibitor Space Using Subpocket-Focused Fragmentation and Recombination. J. Chem. Inf. Model. 2020. https://pubs.acs.org/doi/abs/10.1021/acs.jcim.0c00839](CITATION.bib)
 
 ```bib
 @article{doi:10.1021/acs.jcim.0c00839,
@@ -111,3 +118,33 @@ note ={PMID: 33155465},
 URL = {https://doi.org/10.1021/acs.jcim.0c00839}
 }
 ```
+## List of publications
+- **Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches**
+Lizhao Hu, Yuyao Yang, Shuangjia Zheng, Jun Xu, Ting Ran, and Hongming Chen
+*Journal of Chemical Information and Modeling* **2021**
+[10.1021/acs.jcim.1c00608](https://pubs.acs.org/doi/full/10.1021/acs.jcim.1c00608)
+- **TWN-FS method: A novel fragment screening method for drug discovery** 
+Yoon, Hye Ree and Park, Gyoung Jin and Balupuri, Anand and Kang, Nam Sook
+*Computational and Structural Biotechnology Journal* **2023**
+[10.1016/j.csbj.2023.09.037](https://doi.org/10.1016/j.csbj.2023.09.037)
+- **Efficient Hit-to-Lead Searching of Kinase Inhibitor Chemical Space via Computational Fragment Merging**
+Grigorii V. Andrianov, Wern Juin Gabriel Ong, Ilya Serebriiskii, and John Karanicolas
+*Journal of Chemical Information and Modeling* **2021** 
+[10.1021/acs.jcim.1c00630](https://doi.org/10.1021/acs.jcim.1c00630)
+- **KiSSim: Predicting Off-Targets from Structural Similarities in the Kinome**
+Dominique Sydow, Eva Aßmann, Albert J. Kooistra, Friedrich Rippmann, and Andrea Volkamer
+*Journal of Chemical Information and Modeling* **2022** 
+[10.1021/acs.jcim.2c00050](https://10.1021/acs.jcim.2c00050)
+- **Target-Focused Library Design by Pocket-Applied Computer Vision and Fragment Deep Generative Linking**
+Merveille Eguida, Christel Schmitt-Valencia, Marcel Hibert, Pascal Villa, and Didier Rognan
+*Journal of Medicinal Chemistry* **2022** 
+[10.1021/acs.jmedchem.2c00931](https://pubs.acs.org/doi/10.1021/acs.jmedchem.2c00931)
+- **Guided docking as a data generation approach facilitates structure-based machine learning on kinases**
+Backenköhler M, Groß J, Wolf V, Volkamer A. 
+*ChemRxiv* **2023**
+[10.26434/chemrxiv-2023-prk53](https://chemrxiv.org/engage/chemrxiv/article-details/658441f7e9ebbb4db96d98e8)  *This content is a preprint and has not been peer-reviewed.*
+- **Constructing Innovative Covalent and Noncovalent Compound Libraries: Insights from 3D Protein–Ligand Interactions** Xiaohe Xu, Weijie Han, Xiangzhen Ning, Chengdong Zang, Chengcheng Xu, Chen Zeng, Chengtao Pu, Yanmin Zhang, Yadong Chen, and Haichun Liu *Journal of Chemical Information and Modeling* **2024**[10.1021/acs.jcim.3c01689](https://pubs.acs.org/doi/10.1021/acs.jcim.3c01689)
+
+
+
+

data/README.md

@@ -1,8 +1,10 @@
 # Data
 
-Overview on data content.
+**Note**: Our fragmentation library is currently based on KLIFS downloaded on *06.12.2023*. 
 
-- `fragment_library/`: Full fragment library resulting from the KinFragLib fragmentation procedure comprises of about 3000 fragments, which are the basis for exploring the subpocket-based chemical space of ligands co-crystallized with kinases.
+Overview of data content:
+
+- `fragment_library/`: Full fragment library resulting from the KinFragLib fragmentation procedure comprises about 3000 fragments, which are the basis for exploring the subpocket-based chemical space of ligands co-crystallized with kinases.
 - `fragment_library_filtered/`: Filtered fragment library: Select fragments tailored for the recombination (remove pool X, deduplicate per subpocket, remove unfragmented ligands, remove all fragments that connect only to pool X, keep only fragment-like fragments, and filter for hinge-like AP fragments).
 - `fragment_library_reduced/`: Reduced fragment library: Select a diverse set of fragments (per subpocket) for recombination starting from the filtered fragment library.
 - `combinatorial_library/`: Combinatorial library based on the reduced fragment library.

data/combinatorial_library/README.md

@@ -1,17 +1,17 @@
 # KinFragLib: Combinatorial library 
 
-[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3956580.svg)](https://doi.org/10.5281/zenodo.3956580)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10843763.svg)](https://doi.org/10.5281/zenodo.10843763)
 
 This folder is meant for the metadata and properties of the KinFragLib combinatorial library, which is based on the KinFragLib fragment library at https://github.com/volkamerlab/KinFragLib. This dataset is used for the analysis of the combinatorial library.
 
-**Note**: Since this dataset contains large files, we provide it outside this repository at https://zenodo.org/record/3956580 (DOI: 10.5281/zenodo.3956580, v1.0.1).
+**Note**: Since this dataset contains large files, we provide it outside this repository at https://zenodo.org/record/10843763 (DOI: 10.5281/zenodo.10843763, v2.0.0).
 In order to run the analysis notebooks, please download this dataset to this folder. 
 
 ## Raw data
 
 - `combinatorial_library.json`: Full combinatorial library, please refer to `notebooks/4_1_combinatorial_library_data_preparation.ipynb` at https://github.com/volkamerlab/KinFragLib for detailed information about this data format
 - `combinatorial_library_deduplicated.json`: Deduplicated combinatorial library (based on InChIs)
-- `chembl_standardized_inchi.csv`: Standardized ChEMBL 25 molecules in the form of InChI strings.
+- `chembl_standardized_inchi.csv`: Standardized ChEMBL 33 molecules in the form of InChI strings.
 
 ## Processed data